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Cultural Vocal Bursts Intensity Prediction | State-of-the-Art

cultural-vocal-bursts-intensity-prediction

Cultural Vocal Bursts Intensity Prediction

3260 papers • 126 benchmarks • 313 datasets

to predict the intensity of 40 culture-specific emotions (10 emotions from each culture)

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in cultural-vocal-bursts-intensity-prediction

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Dataset

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HUME-VB

Libraries

Use these libraries to find cultural-vocal-bursts-intensity-prediction models and implementations

castorini/pyserini

2 papers 1,426

Datasets

HUME-VB

Subtasks

No subtasks available.

Most implemented papers

Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter

T. Alshaabi, J. L. Adams, M. V. Arnold, J. Minot, D. R. Dewhurst, A. J. Reagan, C. Danforth, P. Dodds•Fri Jul 24 2020

The method of tracking dynamic changes in n-grams can be extended to any temporally evolving corpus, and example use cases including social amplification, the sociotechnical dynamics of famous individuals, box office success, and social unrest are presented.

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Introduction Benchmarks Datasets Subtasks Libraries Papers

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Changing Fashion Cultures

Kaori Abe, Teppei Suzuki, S. Ueta, Akio Nakamura, Y. Satoh, Hirokatsu Kataoka•Wed Mar 22 2017

The paper created a new fashion culture database (FCDB), which consists of 76 million geo-tagged images in 16 cosmopolitan cities and proposes an unsupervised fashion trend descriptor (FTD) using a fashion descriptor, a codeword vetor, and temporal analysis to unveil fashion trends in the FCDB.

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r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection

W. Wang, Sharon Levy, Kai Nakamura•Sat Nov 09 2019

This work presents Fakeddit, a novel multimodal dataset consisting of over 1 million samples from multiple categories of fake news, and constructs hybrid text+image models and performs extensive experiments for multiple variations of classification, demonstrating the importance of the novel aspect of multimodality and fine-grained classification unique to Fakeddam.

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Proceedings of the ACII Affective Vocal Bursts Workshop and Competition 2022 (A-VB): Understanding a critically understudied modality of emotional expression

Alan S. Cowen, Alice Baird, B. Schuller, Panagiotis Tzirakis, D. Keltner, Jeffrey A. Brooks, Christopher B. Gregory, A. Batliner•Wed Oct 26 2022

This is the Proceedings of the ACII Affective Vocal Bursts Workshop and Competition, where the participants were presented with four emotion-focused sub-challenges that utilize the large-scale and `in-the-wild' Hume-VB dataset.

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A Century of Portraits: A Visual Historical Record of American High School Yearbooks

Alexei A. Efros, Philipp Krähenbühl, Shiry Ginosar, Kate Rakelly, S. Sachs, Brian Yin, Crystal Lee•Sun Nov 08 2015

A dataset of 37,921 frontal-facing American high school yearbook photos is presented that allows us to use computation to glimpse into the historical visual record too voluminous to be evaluated manually and may be used together with weakly-supervised data-driven techniques to perform scalable historical analysis of large image corpora with minimal human effort.

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Survey of Artificial Intelligence for Card Games and Its Application to the Swiss Game Jass

Joel Niklaus, M. Liwicki, Michele Alberti, Vinaychandran Pondenkandath, R. Ingold•Fri May 31 2019

This work provides an overview of the current state-of-the-art of Artificial Intelligence methods for card games in general and their application to the use-case of the Swiss card game Jass.

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Sentimental LIAR: Extended Corpus and Deep Learning Models for Fake Claim Classification

Vahid Behzadan, Bibek Upadhayay•Mon Aug 31 2020

This paper introduces Sentimental LIAR, which extends the LIAR dataset of short claims by adding features based on sentiment and emotion analysis of claims, and proposes a novel deep learning architecture based on the BERT-Base language model for classification of claims as genuine or fake.

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Socially Aware Bias Measurements for Hindi Language Representations

Nanyun Peng, Kai-Wei Chang, Vijit Malik, Sunipa Dev, A. Nishi•Thu Oct 14 2021

This work investigates the biases present in Hindi language representations such as caste and religion associated biases and demonstrates how biases are unique to specific language representations based on the history and culture of the region they are widely spoken in.

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UTSig: A Persian offline signature dataset

Babak Nadjar Araabi, Amir Soleimani, K. Fouladi•Wed Mar 09 2016

Results of the experiments show that training genuine samples along with opposite-hand samples and random forgeries can improve the performance in terms of equal error rate and minimum cost of log likelihood ratio.

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